Single-cell Sequencing Research Articles

Integrating multiomics and Single-Cell communication analysis to uncover Ankylosing spondylitis mechanisms

BackgroundAnkylosing spondylitis (AS) is a chronic inflammatory joint disorder, necessitating early diagnosis and effective treatment. The specific mechanism of action of Cassia twigs in the treatment of AS is not fully understood. MethodsBlood samples and clinical data from 28,458 individuals (6,101 with AS, 22,357 without AS) were collected. To construct a predictive model, we utilized logistic regressions and machine learning techniques to create a dynamic nomogram. Immune cell infiltration was evaluated using the GSE73754 dataset. Subsequently, we obtained vertebral bone marrow blood from AS patients for 10X single-cell sequencing. We also extracted and purified total RNA from hip joint ligament tissue samples from six AS patients and six non-AS patients. The genes related to the expression of AS and Cassia twigs were analyzed comprehensively, and the specific drug targets were identified by molecular docking. The interactions between immune cells through cell communication analysis were elucidated. ResultsWe developed a dynamic nomogram incorporating the neutrophil count (NEUT) and other variables. Neutrophil immune responses were confirmed through immune infiltration analysis utilizing GSE73754. We observed the early involvement of neutrophils in the pathology of AS. The CAT-expressing Cassia twigs gene could be used as a drug target for the treatment of AS. Moreover, comprehensive RNA analysis revealed notable CAT expression in neutrophils and various other immune cells. ConclusionsNeutrophils play dual roles in AS, regulating inflammation and initiating differentiation signals to other cells. The CAT gene, which is expressed in Cassia twigs, has emerged as a potential therapeutic target for AS treatment.

Single-cell sequencing analysis reveals the dynamic tumour ecosystems of primary and metastatic lymph nodes in nasopharyngeal carcinoma.

Lymph node metastasis contributed to the leading cause and treatment failure in nasopharyngeal carcinoma (NPC). The microenvironment and the cellular communications of lymph node metastasized tumours determine the tumour progression and therapeutic effect, but the ecosystems about the lymph node metastasis (LNM) for NPC patients remain poorly characterized. Here, we integrated the transcriptomes of 47,618 single cells from eight samples related to NPC LNM. The dynamic immune ecosystems and immunosuppressive microenvironment including T cells, myeloid cells and B cells were observed in the lymph node metastatic samples compared with primary tumours. Additionally, the heterogeneity of epithelial cells was also revealed, and several clusters with expression programs that were associated with the progression-free survival of NPC patients were identified. Additionally, our data revealed the complex intercellular communications from primary to lymph node metastasis. The rewiring of CCL signalling which plays an important role in tumour metastasis was further identified. Altogether, we systematically characterized the ecosystem of NPC primary and lymph node metastasized tumours, which may shed light on the development of a therapeutic strategy to improve clinical outcomes of NPC patients with lymph node metastasis.

Neutrophils with low production of reactive oxygen species are activated during immune priming and promote development of arthritis

Rheumatoid arthritis (RA) is an inflammatory autoimmune disease mediated by immune cell dysfunction for which there is no universally effective prevention and treatment strategy. As primary effector cells, neutrophils are important in the inflammatory joint attack during the development of RA. Here, we used single-cell sequencing technology to thoroughly analyze the phenotypic characteristics of bone marrow-derived neutrophils in type II collagen (COL2)-induced arthritis (CIA) models, including mice primed and boosted with COL2. We identified a subpopulation of neutrophils with high expression of neutrophil cytoplasmic factor 1 (NCF1) in primed mice, accompanied by a characteristic reactive oxygen species (ROS) response, and a decrease in Ncf1 expression in boosted mice with the onset of arthritis. Furthermore, we found that after ROS reduction, arthritis occurred in primed mice but was attenuated in boosted mice. This bidirectional effect of ROS suggested a protective role of ROS during immune priming. Mechanistically, we combined functional assays and metabolomics identifying Ncf1-deficient neutrophils with enhanced migration, chemotactic receptor CXCR2 expression, inflammatory cytokine secretion, and Th1/Th17 differentiation. This alteration was mainly due to the metabolic reprogramming of Ncf1-deficient neutrophils from an energy supply pathway dominated by gluconeogenesis to an inflammatory immune pathway associated with the metabolism of histidine, glycine, serine, and threonine signaling, which in turn induced arthritis. In conclusion, we have systematically identified the functional and inflammatory phenotypic characteristics of neutrophils under ROS regulation, which provides a theoretical basis for understanding the pathogenesis of RA, to further improve prevention strategies and identify novel therapeutic targets.

PLXNB1/SEMA4D signals mediate interactions between malignant epithelial and immune cells to promote colorectal cancer liver metastasis.

Distal metastases result from metastatic microenvironment and tumour epithelial cell interactions, the cellular heterogeneity of primary colorectal cancer (CRC) and liver metastases (LM) was evaluated by integrating single-cell sequencing data, and the collected gene expression data from metastatic epithelial cell subsets was used to construct a prognostic model and to identify intercellular receptor-ligand interactions between epithelial and immune cells in CRC and LM. Multiplex immunofluorescence staining, and invitro wound healing, cell migration and cell apoptosis assays were performed to further explore the biological relevance of identified potential regulatory molecules. In this study, approximately 17 epithelial cell subtypes were detected, with Epi-11 cells being highly expressed in LM tissues compared with CRC samples. Furthermore, patients with high expression of the metastasis-related genetic profile of Epi-11 had a poorer prognosis. By predicting receptor-ligand interactions, Epi-11 cells were found to interact more with myeloid and T/natural killer cells in LM tissues when compared to primary CRC samples, which was mediated by the PLXNB1/SEMA4D axis. In addition, high SEMA4D expression was correlated with decreased overall survival of patients with CRC, whereas PLXNB1 was not. SEMA4D knockdown prevented the migration and promoted the apoptosis of HCT116 cells invitro. In summary, Epi-11 cells, an important subset of epithelial cells, may drive the LM of CRC and act by crosstalk with immune cells through the PLXNB1/SEMA4D signalling axis.

Alterations in intratumoral and peripheral immune status in recurrent gliomas and their prognostic implications for patients underwent reoperation

BackgroundReoperation is a treatment option for recurrent gliomas, yet factors impacting survival following reoperation remain poorly defined. Tumor immunity is profoundly associated with disease progression. Here, we analyze the immune status characteristics and their prognostic implications in recurrent gliomas. MethodsIntratumoral and peripheral immune characteristics between primary and recurrent gliomas were compared by conducting immunohistological staining and hematological examination with our in-house samples, and analyzing bulk and single-cell sequencing data from publicly available sources. Survival analysis was conducted to identify immunological markers with prognostic significance. ResultsWe observed a significant reduction in peripheral lymphocyte count, while an elevation in neutrophil-to-lymphocyte ratio (NLR) and red cell distribution width-to-platelet ratio (RPR) in patients with recurrent gliomas than in newly-diagnosed patients. Higher NLR and RPR indicated worse survival following reoperation in recurrent patients. Transcriptomic and immunohistological analysis showed an increased infiltration of tumor-associated macrophages (TAMs) and CD8+ T cell in recurrent gliomas compared to primary gliomas in both IDH-wildtype and mutant subtypes. Moreover, the abundance of TAMs emerged as an independent indicator for an inferior prognosis in recurrent gliomas. Single-cell profiling revealed a significant heterogeneity in the phenotypes of TAMs between primary and recurrent gliomas. Notably, TAMs enriched in recurrent gliomas exhibited elevated expression of interferon-γ-induced genes, multiple immunosuppressive molecules (TGFB1, CD276), and increased activity in glycose and lipid metabolism, indicating metabolic reprogramming. ConclusionRecurrent gliomas demonstrate augmented immune cell infiltration, but they fail to overcome TAMs-induced immunosuppression. Immunosuppressive indices, including TAM abundance, peripheral NLR and RPR, have prognostic implications for recurrent gliomas.

Exploring the interaction between immune cells in the prostate cancer microenvironment combining weighted correlation gene network analysis and single-cell sequencing: An integrated bioinformatics analysis.

The rise of treatment resistance and variability across malignant profiles has made precision oncology an imperative in today's medical landscape. Prostate cancer is a prevalent form of cancer in males, characterized by significant diversity in both genomic and clinical characteristics. The tumor microenvironment consists of stroma, tumor cells, and various immune cells. The stromal components and tumor cells engage in mutual communication and facilitate the development of a low-oxygen and pro-cancer milieu by producing cytokines and activating pro-inflammatory signaling pathways. In order to discover new genes associated with tumor cells that interact and facilitate a hypoxic environment in prostate cancer, we conducted a cutting-edge bioinformatics investigation. This included analyzing high-throughput genomic datasets obtained from the cancer genome atlas (TCGA). A combination of weighted gene co-expression network analysis and single-cell sequencing has identified nine dysregulated immune hub genes (AMACR, KCNN3, MME, EGFR, FLT1, GDF15, KDR, IGF1, and KRT7) that are believed to have significant involvement in the biological pathways involved with the advancement of prostate cancer enviriment. In the prostate cancer environment, we observed the overexpression of GDF15 and KRT7 genes, as well as the downregulation of other genes. Additionally, the cBioPortal platform was used to investigate the frequency of alterations in the genes and their effects on the survival of the patients. The Kaplan-Meier survival analysis indicated that the changes in the candidate genes were associated with a reduction in the overall survival of the patients. In summary, the findings indicate that studying the genes and their genomic changes may be used to develop precise treatments for prostate cancer. This approach involves early detection and targeted therapy.

Single cell RNA sequencing provides novel cellular transcriptional profiles and underlying pathogenesis of presbycusis

Age-related hearing loss (ARHL) or presbycusis is associated with irreversible progressive damage in the inner ear, where the sound is transduced into electrical signal; but the detailed mechanism remains unclear. Here, we sought to determine the potential molecular mechanism involved in the pathogeneses of ARHL with bioinformatics methods. A single-cell transcriptome sequencing study was performed on the cochlear samples from young and aged mice. Detection of identified cell type marker allowed us to screen 18 transcriptional clusters, including myeloid cells, epithelial cells, B cells, endothelial cells, fibroblasts, T cells, inner pillar cells, neurons, inner phalangeal cells, and red blood cells. Cell-cell communications were analyzed between young and aged cochlear tissue samples by using the latest integration algorithms Cellchat. A total of 56 differentially expressed genes were screened between the two groups. Functional enrichment analysis showed these genes were mainly involved in immune, oxidative stress, apoptosis, and metabolic processes. The expression levels of crucial genes in cochlear tissues were further verified by immunohistochemistry. Overall, this study provides new theoretical support for the development of clinical therapeutic drugs.

Stem-like exhausted CD8 T cells in pleural effusions predict improved survival in non-small cell lung cancer (NSCLC) and mesothelioma.

Anti-tumor CD8 T cells are important for immunity but can become 'exhausted' and hence ineffective. Tumor-infiltrating exhausted CD8+ T cells include less differentiated stem-like exhausted T (Texstem) cells and terminally exhausted T (Texterm) cells. Both subsets have been proposed as prognostic biomarkers in cancer patients. In this study, we retrospectively investigated their prognostic significance in patients with metastatic non-small cell lung cancer (NSCLC) and validated our findings in a mesothelioma cohort. Pre-treatment malignant pleural effusions (PEs) from 43 NSCLC (41 non-squamous, 2 squamous) patients were analyzed by flow cytometry. The percentages of Texstem and Texterm CD8 T cells were correlated with overall survival (OS) after adjusting for clinicopathological variables. Findings were validated using a mesothelioma cohort (n=49). Mass cytometry was performed on 16 pre-treatment PE samples from 5 mesothelioma and 3 NSCLC patients for T-cell phenotyping. Single-cell multi-omics analysis was performed on 4 pre-treatment PE samples from 2 NSCLC patients and 2 mesothelioma patients for analysis of the transcriptomic profiles, surface markers and T cell receptor (TCR) repertoire. Higher frequency of Texstem was associated with significantly increased OS [median 9.9 vs. 3.4 months, hazard ratio (HR) 0.36, 95% CI: 0.16-0.79, P=0.01]. The frequency of Texterm was not associated with OS. These findings were validated in the mesothelioma cohort (high vs. low Texstem, median OS 32.1 vs. 19.8 months, HR 0.31, 95% CI: 0.10-0.96, P=0.04). Detailed single-cell sequencing and mass cytometry profiling revealed that exhausted T cells from NSCLC expressed greater stem-likeness and less inhibitory markers than those from mesothelioma and that Texstem cells also contained 'bystander' virus-specific T cells. This study demonstrates that PE CD8 Texstem cell abundance is associated with better survival outcomes, and thus may be a useful prognostic biomarker.

Single-cell analysis of preimplantation embryonic development in guinea pigs

BackgroundGuinea pigs exhibit numerous physiological similarities to humans, yet the details of their preimplantation embryonic development remain largely unexplored.ResultsTo address this, we conducted single-cell sequencing on the transcriptomes of cells isolated from the zygote stage through preimplantation stages in guinea pigs. This study identified seven distinct cell types within guinea pig preimplantation embryos and pinpointed the timing of zygotic gene activation (ZGA). Trajectory analysis revealed a bifurcation into two lineage-specific branches, accompanied by alterations in specific pathways, including oxidative phosphorylation and vascular endothelial growth factor (VEGF). Additionally, co-expressed gene network analysis highlighted the most enriched functional modules for the epiblast (EPI), primitive endoderm (PrE), and inner cell mass (ICM). Finally, we compared the similarities and differences between human and guinea pig epiblasts (EPIs).ConclusionThis study systematically constructs a cell atlas of guinea pig preimplantation embryonic development, offering fresh insights into mammalian embryonic development and providing alternative experimental models for studying human embryonic development.

KaryoTap Enables Aneuploidy Detection in Thousands of Single Human Cells.

Investigating chromosomal instability and aneuploidy within tumors is essential for understanding tumorigenesis and developing diagnostic and therapeutic strategies. Single-cell DNA sequencing technologies have enabled such analyses, revealing aneuploidies specific to individual cells within the same tumor. However, it has been difficult to scale the throughput of these methods to detect rare aneuploidies while maintaining high sensitivity. To overcome this deficit, we developed KaryoTap, a method combining custom targeted DNA sequencing panels for the Tapestri platform with a computational framework to enable detection of chromosome- and chromosome arm-scale aneuploidy (gains or losses) and copy number neutral loss of heterozygosity in all human chromosomes across thousands of single cells simultaneously. KaryoTap allows detecting gains and losses with an average accuracy of 83% for arm events and 91% for chromosome events. Importantly, together with chromosomal copy number, our system allows us to detect barcodes and gRNAs integrated into the cells' genome, thus enabling pooled CRISPR- or ORF-based functional screens in single cells. As a proof of principle, we performed a small screen to expand the chromosomes that can be targeted by our recently described CRISPR-based KaryoCreate system for engineering aneuploidy in human cells. KaryoTap will prove a powerful and flexible approach for the study of aneuploidy and chromosomal instability in both tumors and normal tissues.

Selection promotes age-dependent degeneration of the mitochondrial genome.

Somatic mutations in mitochondrial genomes (mtDNA) accumulate exponentially during aging. Using single cell sequencing, we characterize the spectrum of age-accumulated mtDNA mutations in mouse and human liver and identify directional forces that accelerate the accumulation of mutations beyond the rate predicted by a neutral model. "Driver" mutations that give genomes a replicative advantage rose to high cellular abundance and carried along "passenger" mutations, some of which are deleterious. In addition, alleles that alter mtDNA-encoded proteins selectively increased in abundance overtime, strongly supporting the idea of a "destructive" selection that favors genomes lacking function. Overall, this combination of selective forces acting in hepatocytes promotes somatic accumulation of mutations in coding regions of mtDNA that are otherwise conserved in evolution. We propose that these selective processes could contribute to the population prevalence of mtDNA mutations, accelerate the course of heteroplasmic mitochondrial diseases and promote age-associated erosion of the mitochondrial genome.

Full-length single-cell BCR sequencing paired with RNA sequencing reveals convergent responses to pneumococcal vaccination

Single-cell RNA sequencing (scRNA-seq) can resolve transcriptional features from individual cells, but scRNA-seq techniques capable of resolving the variable regions of B cell receptors (BCRs) remain limited, especially from widely-used 3′-barcoded libraries. Here, we report a method that can recover paired, full-length variable region sequences of BCRs from 3′-barcoded scRNA-seq libraries. We first verify this method (B3E-seq) can produce accurate, full-length BCR sequences. We then apply this method to profile B cell responses elicited against the capsular polysaccharide of Streptococcus pneumoniae serotype 3 (ST3) by glycoconjugate vaccines in five infant rhesus macaques. We identify BCR features associated with specificity for the ST3 antigen which are present in multiple vaccinated monkeys, indicating a convergent response to vaccination. These results demonstrate the utility of our method to resolve key features of the B cell repertoire and profile antigen-specific responses elicited by vaccination.

DRCTdb: disease-related cell type analysis to decode cell type effect and underlying regulatory mechanisms.

Understanding the molecular mechanisms underlying genetic diseases is challenging due to environmental and genetic factors. Genome-wide association studies (GWAS) have identified numerous genetic loci, but their functional implications are largely unknown. Single-cell multiomics sequencing has emerged as a powerful tool to study disease-specific cell types and their relationship with genetic variants. However, comprehensive databases for exploring these mechanisms across different tissues are lacking. We present the Disease-Related Cell Type database (DRCTdb), integrating GWAS and single-cell multiomics data to identify disease-related cell types and elucidate their regulatory mechanisms. DRCTdb contains well-processed data from 16 studies, covering 4 million cells within 28 tissues. Users can browse relationships and regulatory mechanisms between SNPs of 42 genetic diseases and cell types based on GWAS and single-cell data. DRCTdb also offers data downloads and is available at https://singlecellatlas.top/DRCTDB .

Deciphering the role of sphingolipid metabolism in the immune microenvironment and prognosis of esophageal cancer via single-cell sequencing and bulk data analysis

BackgroundEsophageal squamous cell carcinoma (ESCC) stands as a significant global health challenge, distinguished by its aggressive progression from the esophageal epithelium. Central to this malignancy is sphingolipid metabolism, a critical pathway that governs key cellular processes, including apoptosis and immune regulation, thereby influencing tumor behavior. The advent of single-cell and transcriptome sequencing technologies has catalyzed significant advancements in oncology research, offering unprecedented insights into the molecular underpinnings of cancer.MethodsWe explored sphingolipid metabolism-related genes in ESCC using scRNA-seq data from GEO and transcriptome data from TCGA. We assessed 97 genes in epithelial cells with AUCell, UCell, and singscore algorithms, followed by bulk RNA-seq and differential analysis to identify prognosis-related genes. Immune infiltration and potential immunotherapeutic strategies were also investigated, and tumor gene mutations and drug treatment strategies were analyzed.ResultOur study identified distinct gene expression patterns, highlighting ARSD, CTSA, DEGS1, and PPTQ's roles in later cellular stages. We identified seven independent prognostic genes and created a precise nomogram for prognosis.ConclusionThis study integrates single-cell and transcriptomic data to provide a reliable prognostic model associated with sphingolipid metabolism and to inform immunotherapy and pharmacotherapy for ESCC at the genetic level. The findings have significant implications for precision therapy in esophageal cancer.

Mugen-UMAP: UMAP visualization and clustering of mutated genes in single-cell DNA sequencing data

BackgroundThe application of Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction and visualization has revolutionized the analysis of single-cell RNA expression and population genetics. However, its potential in single-cell DNA sequencing data analysis, particularly for visualizing gene mutation information, has not been fully explored.ResultsWe introduce Mugen-UMAP, a novel Python-based program that extends UMAP’s utility to single-cell DNA sequencing data. This innovative tool provides a comprehensive pipeline for processing gene annotation files of single-cell somatic single-nucleotide variants and metadata to the visualization of UMAP projections for identifying clusters, along with various statistical analyses. Employing Mugen-UMAP, we analyzed whole-exome sequencing data from 365 single-cell samples across 12 non-small cell lung cancer (NSCLC) patients, revealing distinct clusters associated with histological subtypes of NSCLC. Moreover, to demonstrate the general utility of Mugen-UMAP, we applied the program to 9 additional single-cell WES datasets from various cancer types, uncovering interesting patterns of cell clusters that warrant further investigation. In summary, Mugen-UMAP provides a quick and effective visualization method to uncover cell cluster patterns based on the gene mutation information from single-cell DNA sequencing data.ConclusionsThe application of Mugen-UMAP demonstrates its capacity to provide valuable insights into the visualization and interpretation of single-cell DNA sequencing data. Mugen-UMAP can be found at https://github.com/tengchn/Mugen-UMAP

High-Throughput Single-Cell Metabolic Labeling, Sorting, and Sequencing of Active Antibiotic-Resistant Bacteria in the Environment.

Active antibiotic-resistant bacteria (ARB) play a major role in spreading antimicrobial resistance (AMR) in the environment; however, they have remained largely unexplored. Herein, we coupled bio-orthogonal noncanonical amino acid tagging with high-throughput fluorescence-activated single-cell sorting (FACS) and sequencing to characterize the phenome and genome of active ARB in complex environmental matrices. Active ARB, conferring resistance to six antibiotics throughout wastewater treatment, were distinguished and quantified. The percentage and concentration of active ARB ranged from 0.28% to 45.3% and from 1.1 × 104 to 2.09 × 107 cells/mL, respectively. Notably, the final effluents retained up to 4.79 × 104 cells/mL of active ARB. Targeted FACS and genomic sequencing revealed a distinct taxonomic composition of active ARB compared with that of the overall population. The coexistence of antibiotic resistome and mobilome in active ARB was also identified, including three high-quality metagenomic assembly genomes assigned to pathogenic bacteria, highlighting the substantial health risks due to their activity, phenotypic resistance, mobility, and pathogenicity. This study advances our understanding of previously overlooked active ARB in the environment by linking their resistance phenotype to their genotype. This high-throughput method will enable efficient quantitative surveillance of active AMR, providing valuable insights into risk control and management.

Macrophage-Expressed Coagulation Factor VII Promotes Adverse Cardiac Remodeling.

Excess fibrotic remodeling causes cardiac dysfunction in ischemic heart disease, driven by MAP (mitogen-activated protein) kinase-dependent TGF-ß1 (transforming growth factor-ß1) activation by coagulation signaling of myeloid cells. How coagulation-inflammatory circuits can be specifically targeted to achieve beneficial macrophage reprogramming after myocardial infarction (MI) is not completely understood. Mice with permanent ligation of the left anterior descending artery were used to model nonreperfused MI and analyzed by single-cell RNA sequencing, protein expression changes, confocal microscopy, and longitudinal monitoring of recovery. We probed the role of the tissue factor (TF)-FVIIa (activated factor VII)-integrin ß1-PAR2 (protease-activated receptor 2) signaling complex by utilizing genetic mouse models and pharmacological intervention. Cleavage-insensitive PAR2R38E and myeloid cell integrin ß1-deficient mice had improved cardiac function after MI compared with controls. Proximity ligation assays of monocytic cells demonstrated that colocalization of FVIIa with integrin ß1 was diminished in monocyte/macrophage FVII-deficient mice after MI. Compared with controls, F7fl/fl CX3CR1 (CX3C motif chemokine receptor 1)Cre mice showed reduced TGF-ß1 and MAP kinase activation, as well as cardiac dysfunction after MI, despite unaltered overall recruitment of myeloid cells. Single-cell mRNA sequencing of CD45 (cluster of differentiation 45)+ cells 3 and 7 days after MI uncovered a trajectory from recruited monocytes to inflammatory TF+/TREM (triggered receptor expressed on myeloid cells) 1+ macrophages requiring F7. As early as 7 days after MI, macrophage F7 deletion led to an expansion of reparative Olfml 3 (olfactomedin-like protein 3)+ macrophages and, conversely, to a reduction of TF+/TREM1+ macrophages, which were also reduced in PAR2R38E mice. Short-term treatment from days 1 to 5 after nonreperfused MI with a monoclonal antibody inhibiting the macrophage TF-FVIIa-PAR2 signaling complex without anticoagulant activity improved cardiac dysfunction, decreased excess fibrosis, attenuated vascular endothelial dysfunction, and increased survival 28 days after MI. Extravascular TF-FVIIa-PAR2 complex signaling drives inflammatory macrophage polarization in ischemic heart disease. Targeting this signaling complex for specific therapeutic macrophage reprogramming following MI attenuates cardiac fibrosis and improves cardiovascular function.

Construction of store-operated calcium entry-related gene signature for predicting prognosis and indicates immune microenvironment infiltration in stomach adenocarcinomas

Gastric adenocarcinoma (STAD) is the most prevalent malignancy of the human digestive system and the fourth leading cause of cancer-related death. Calcium pools, especially Ca2+ entry (SOCE) for storage operations, play a crucial role in maintaining intracellular and extracellular calcium balance, influencing cell activity, and facilitating tumor progression. Nevertheless, the prognostic and immunological value of SOCE in STAD has not been systematically studied. The objective of this study was to develop a risk model for SOCE signature and to explore its correlation with clinical characteristics, prognosis, tumor microenvironment (TME), as well as response to immunotherapy, chemotherapy, and targeted drugs. We used the TCGA, GEO (GSE84437 and GSE159929), cBioPortal and TIMER databases to acquire mRNA expression data for STAD, along with patient clinical indicators, single-cell sequencing data, genomic variation information, and correlations of immune cell infiltration. An analysis of SOCE genes based on tumor vs. normal tissue comparisons, pan-cancer dimension, single-cell sequencing, DNA mutation, and copy number variation revealed that SOCE genes significantly impact the survival of STAD patients and are differentially involved in the immune response. SOCE co-expressed genes were identified by Pearson analysis, and subsequently protein-protein interaction (PPI) and gene function enrichment analysis indicated that coexpressed genes were associated with multicellular pathways. Based on TCGA and GSE84437 datasets, a multifactor Cox proportional hazard regression analysis was conducted to identify SOCE co-expressed genes associated with overall survival in STAD patients. Several mRNA prognostic genes, including PTPRJ, GPR146, LTBP3, FBLN1, EFEMP2, ADAMTS7 and LBH, were identified, which could be used as effective prognostic predictors for STAD patients. In both training and test datasets, receiver operating characteristic (ROC) curves were utilized to evaluate and illustrate the predictive capability of this characteristic in forecasting overall survival of STAD patients. The qPCR and human protein atlas (HPA) were employed to assess mRNA expression and protein levels. Furthermore, the ESTIMATE, TIMER, CIBERSORT, QUANTISEQ, MCPCOUNTER, xCell and EPIC algorithms were utilized to perform immune score and analyze immune cell infiltration. It effectively revealed the difference in prognosis and immune cell infiltration in TME between high-risk and low-risk groups based on the risk signature associated with SOCE. Notably, significant differences in tumor immune dysfunction and rejection (TIDE) scores between the two groups, suggesting that patients in the low-risk group may exhibit a more favorable response to ICIS treatment. The GDSC database and R packages for predictive analysis were utilized to analyze responses to chemotherapy and targeted drugs in high-risk and low-risk groups. In summary, the 7-gene signature associated with SOCE serves as a significant biomarker for evaluating the TME and predicting the prognosis of STAD patients. In addition, it may provide valuable insights for developing effective immunotherapy and chemotherapy regiments for patients with STAD.

Identification of immune patterns in idiopathic pulmonary fibrosis patients driven by PLA2G7-positive macrophages using an integrated machine learning survival framework

Patients with advanced idiopathic pulmonary fibrosis (IPF), a complex and incurable lung disease with an elusive pathology, are nearly exclusive candidates for lung transplantation. Improved identification of patient subtypes can enhance early diagnosis and intervention, ultimately leading to better prognostic outcomes for patients. The goal of this study is to identify new immune patterns and biomarkers in patients. Immune subtypes in IPF patients were identified using single-sample gene set enrichment analysis, and immune subtype-related genes were explored using the weighted correlation network analysis algorithm. A machine learning integration framework was used to establish the optimal prognostic model, known as the immune-related risk score (IRS). Single-cell sequencing was conducted to investigate the major role of macrophage-derived PLA2G7 in the immune microenvironment. We assessed the stability of celecoxib in targeting PLA2G7 through molecular docking and surface plasmon resonance. IPF patients present two distinct immune subtypes, one characterized by immune activation and inflammation, and the other by immune suppression. IRS can predict the immune status and prognosis of IPF patients. Furthermore, multi-cohort analysis and single-cell sequencing analysis demonstrated the diagnostic and prognostic value of PLA2G7 derived from macrophages and its role in shaping the inflammatory immune microenvironment in IPF patients. Celecoxib could effectively and stably bind with PLA2G7. PLA2G7, as identified through IRS, demonstrates marked stability in diagnosing and predicting the prognosis of IPF patients as well as predicting their immune status. It can serve as a novel biomarker for IPF patients.

Somatic mitochondrial DNA mutations are a source of heterogeneity among primary leukemic cells.

Somatic mitochondrial DNA (mtDNA) mutations are prevalent in tumors, yet defining their biological significance remains challenging due to the intricate interplay between selective pressure, heteroplasmy, and cell state. Utilizing bulk whole-genome sequencing data from matched tumor and normal samples from two cohorts of pediatric cancer patients, we uncover differences in the accumulation of synonymous and nonsynonymous mtDNA mutations in pediatric leukemias, indicating distinct selective pressures. By integrating single-cell sequencing (SCS) with mathematical modeling and network-based systems biology approaches, we identify a correlation between the extent of cell-state changes associated with tumor-enriched mtDNA mutations and the selective pressures shaping their distribution among individual leukemic cells. Our findings also reveal an association between specific heteroplasmic mtDNA mutations and cellular responses that may contribute to functional heterogeneity among leukemic cells and influence their fitness. This study highlights the potential of SCS strategies for distinguishing between pathogenic and passenger somatic mtDNA mutations in cancer.